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4 Trends Shaping the Future of Generative AI


Generative AI in the Enterprise: Four Trends to Watch

Pitchbook predicts that the market for generative AI in the enterprise will grow at a 32% CAGR to reach $98.1 billion by 2026. The launch of ChatGPT late last year has accelerated the growth of generative AI, with major tech players like Microsoft, Google, and Salesforce racing to integrate the technology into their platforms. As the pace of innovation continues to pick up, it’s essential to examine the trends that are shaping the future of generative AI in the enterprise.

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1. Attention will shift to training generative AI on enterprise data

Most generative AI tools work exclusively on publicly-available data, but there is a whole other world of possibility that opens up when generative AI is trained on enterprise data. Enterprise data is growing at an explosive rate, yet over 80% of it is unstructured, making it difficult for employees to find and use. Companies are increasingly turning to generative AI to easily search for and surface data within internal files and systems.

2. Integration will be a key enterprise value driver

Currently, innovation is occurring within specific platforms, but the majority of a company’s data lives in various systems. The enterprise value of generative AI grows exponentially when combined with federated search, which can pull data from a company’s entire set of tools and respond to a question or surface the information needed in the moment.

3. Companies will start to establish generative AI strategies, policies, and standards

As companies adopt generative AI, teams leading the strategy and implementation will need to determine where it makes the most sense to augment existing applications, where to build new ones, and where to invest in packaged applications. Organizations will also need to establish policies on how to use the technology and will need to identify and adhere to the right compliance standards.

4. Accuracy will rule

Generative AI occasionally makes up answers, known as “hallucination,” when there is not enough content upon which to base a response. Accuracy and evidence for answers will become key factors in providers of generative AI tools. The ability to validate responses and provide accurate information will be essential for companies to adopt generative AI.

The New Frontier of AI

The future of generative AI for the enterprise is very bright. As practical applications continue to emerge, companies are seeing unprecedented efficiencies and competitive advantages. To stay ahead of the curve, organizations need to invest in use cases that will deliver sustainable value for their business.

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What is generative AI?

Generative AI refers to technology that uses neural networks and machine learning to create its output. Unlike traditional AI systems that are programmed to provide specific outputs, generative AI can create new, original content based on a dataset it has been trained on.

What are some use cases for generative AI in the enterprise?

Generative AI can be used for a variety of tasks, including language translation, creative writing, and content creation. In the enterprise, it can be used to search and surface internal data, generate reports, and streamline workflows.

What are the challenges with implementing generative AI in the enterprise?

One of the main challenges is ensuring accuracy and compliance. Generative AI occasionally makes up answers, and it can be difficult to validate responses. Additionally, organizations need to establish policies on how to use the technology and identify the right compliance standards.


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